NDLI: A Computationally Efficient Algorithm for Building Statistical Color Models

Content Provider	IEEE Xplore Digital Library
Author	Mingzhi Dong Liang Yin Weihong Deng Jun Guo Weiran Xu
Copyright Year	2012
Abstract	Though widely used in surveillance systems of human or fire detection, statistical color models suffer from long training time during parametric estimation. To solve this low-dimension huge-number density estimation problem, we propose a computationally efficient algorithm: weighted EM, which learns the parameters of finite mixture distribution from the histogram of training data. Thus by representing data with a small number of parameters, we significantly reduce long-time storage costs. At the same time, estimating parameters from the histogram of relatively small size ensures the computational efficiency. The algorithm can be readily applied to any mixture model which can be estimated by EM and its online learning form is also given in our paper. In the experiment of skin detection, the algorithm is tested in a database of nearly half a billion training samples, and the results show that our algorithm can do density estimation accurately and enjoys significantly better computational and storage efficiency.
Starting Page	402
Ending Page	407
File Size	247520
Page Count	6
File Format	PDF
ISBN	9781467320276
DOI	10.1109/ICMEW.2012.76
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Histograms Adaptation models Image color analysis Low-dimension Huge-amount Parametric Estimation Computational modeling Weighted EM Estimation Skin Detection Skin Statistical Color Models
Content Type	Text
Resource Type	Article

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